Microsensors, arrays and automatic diagnosis of sensor faults

E Gaura*, R. M. Newman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

As man-made dynamical systems become increasingly complex, there is an ever-present need to ensure their safe and reliable operation. These requirements extend beyond the normally accepted safety-critical systems (e.g. nuclear reactors, chemical plants, and aircraft) to new systems such as autonomous vehicles and rapid transport systems. Early detection of faults and/or malfunctions in industrial processes and systems can help reduce downtimes and the incidence of catastrophic events. Sensors are essential components of any process or system which makes use of automatic control. It follows that an important aspect of any process/system fault diagnosis strategy is to attempt to determine their state of functionality.

The paper opens a discussion on the appropriateness of local sensor health monitoring, fault diagnosis and measurement confidence indices. It looks at the techniques currently used for process fault detection, both centralised and hierarchical, and explores further the possibilities of transposing some of the design concepts from macrosystem level to microsystems, in respect to fault diagnosis.

The use of Artificial Intelligence techniques is suggested for implementing on-chip sensor diagnosis. Micromachined accelerometers are considered as a case study.

Original languageEnglish
Title of host publicationNanotech 2003, Vol 1
EditorsM Laudon, B Romanowicz
PublisherComputational Publications
Pages276-279
Number of pages4
ISBN (Print)0972842209
Publication statusPublished - 2003
EventNanotechnology Conference and Trade Show (Nanotech 2003) - San Francisco, Canada
Duration: 23 Feb 200327 Feb 2003

Conference

ConferenceNanotechnology Conference and Trade Show (Nanotech 2003)
CountryCanada
CitySan Francisco
Period23/02/0327/02/03

Keywords

  • sensors
  • fault diagnosis
  • artificial intelligence

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